Characterizing meniscus behavior in 3D liquid metal printing

ABSTRACT

A 3D printer includes a nozzle and a camera configured to capture an image, a video, or both of a plurality of drops of liquid metal being jetted through the nozzle. The 3D printer also includes a computing system configured to measure a signal proximate to the nozzle based at least partially upon the image, the video, or both. The computing system is also configured to determine one or more metrics that characterize a behavior of the drops based at least partially upon the signal.

TECHNICAL FIELD

The present teachings relate generally to three-dimensional (3D)printing and, more particularly, to characterizing the behavior of themeniscus of liquid metal when the liquid metal is in a nozzle of the 3Dprinter.

BACKGROUND

A 3D printer builds (e.g., prints) a 3D object from a computer-aideddesign (CAD) model, usually by successively depositing material layerupon layer. For example, a first layer may be deposited upon asubstrate, and then a second layer may be deposited upon the firstlayer. One particular type of 3D printer is a magnetohydrodynamic (MHD)printer, which is suitable for depositing liquid metal layer upon layerto form a 3D metallic object. Magnetohydrodynamic refers to the study ofthe magnetic properties and the behavior of electrically conductingfluids.

In a MHD printer, an electrical current flows through a metal coil,which produces time-varying magnetic fields that induce eddy currentswithin a reservoir of liquid metal compositions. Coupling betweenmagnetic and electric fields within the liquid metal results in Lorentzforces that cause drops of the liquid metal to be ejected (also referredto as jetted) through a nozzle of the printer. The nozzle may becontrolled to select the size and shape of the drops. The drops landupon the substrate and/or the previously deposited drops to cause theobject to grow in size.

SUMMARY

The following presents a simplified summary in order to provide a basicunderstanding of some aspects of one or more embodiments of the presentteachings. This summary is not an extensive overview, nor is it intendedto identify key or critical elements of the present teachings, nor todelineate the scope of the disclosure. Rather, its primary purpose ismerely to present one or more concepts in simplified form as a preludeto the detailed description presented later.

A method is disclosed. The method includes capturing an image, a video,or both of a plurality of drops being jetted through a nozzle of aprinter. The method also includes measuring a signal proximate to thenozzle based at least partially upon the image, the video, or both. Themethod also includes determining one or more metrics that characterize abehavior of the drops based at least partially upon the signal.

A method for printing is also disclosed. The method includes capturing avideo of a plurality of drops being jetted through a nozzle of aprinter. The method also includes determining a spatiotemporal variance(STV) signal proximate to a location of the nozzle in the video. Themethod also includes determining a plurality of pulse periods based atleast partially upon the STV signal. Each pulse period includes aportion of the STV signal between two consecutive drops of the pluralityof drops. The method also includes generating a pulse-averaged signalbased at least partially upon the plurality of pulse periods. The methodalso includes generating an amplitude envelope based at least partiallyupon the pulse-averaged signal. The method also includes generating ameniscus carrier signal based at least partially upon the pulse-averagedsignal, the amplitude envelope, or both. The method also includesdetermining a meniscus oscillation frequency of the drops in the nozzlebased at least partially upon the meniscus carrier signal.

A method for characterizing a behavior of a plurality of drops of aliquid while the drops are positioned at least partially within a nozzleof a printer is also disclosed. The method includes capturing a video ofthe plurality of drops of the liquid being jetted through the nozzle ofthe printer. The method also includes determining a location of thenozzle in the video. The method also includes determining aspatiotemporal variance (STV) signal at the location of the nozzle inthe video. The method also includes determining when the drops arejetted through the nozzle by identifying a neighboring locationproximate to the location of the nozzle, determining a second STV signalat the neighboring location, and determining that the drops are jettedthrough the nozzle in response to increases in the second STV signal,which indicates that the drops have been jetted through the nozzle andare passing through the neighboring location. The method also includesdetermining a plurality of pulse periods based at least partially uponthe determination of when the drops are jetted through the nozzle. Eachpulse period includes a portion of the STV signal between twoconsecutive drops of the plurality of drops. The method also includesgenerating a pulse-averaged signal by aligning and averaging theplurality of pulse periods. The method also includes generating anamplitude envelope by moving a sliding temporal window over thepulse-averaged signal. The amplitude envelope includes a differencebetween local maxima and minima over the sliding temporal window. Themethod also includes generating a meniscus carrier signal by normalizingthe pulse-averaged signal to zero mean to produce a normalizedpulse-averaged signal, and dividing the normalized pulse-averaged signalby the envelope amplitude to generate the meniscus carrier signal. Themeniscus carrier signal is in a time domain. The method also includesdetermining a meniscus oscillation frequency of menisci on lowersurfaces of the drops when the drops are positioned at least partiallyin the nozzle by converting the meniscus carrier signal from the timedomain to a frequency domain, and locating a peak of the meniscuscarrier signal in the frequency domain. The method also includesadjusting a parameter of the printer based at least partially upon themeniscus oscillation frequency.

A 3D printer is also disclosed. The 3D printer includes a nozzle. The 3Dprinter also includes a camera configured to capture an image, a video,or both of a plurality of drops of liquid metal being jetted through thenozzle. The 3D printer also includes a computing system configured tomeasure a signal proximate to the nozzle based at least partially uponthe image, the video, or both. The computing system is also configuredto determine one or more metrics that characterize a behavior of thedrops based at least partially upon the signal.

A 3D printer configured to print a 3D object is also disclosed. The 3Dprinter includes a nozzle. The 3D printer also includes a cameraconfigured to capture a video of a plurality of drops of liquid metalbeing jetted through the nozzle. The 3D printer also includes acomputing system configured to determine a spatiotemporal variance (STV)signal at a location of the nozzle in the video. The computing system isalso configured to determine a plurality of pulse periods based at leastpartially upon the STV signal. Each pulse period includes a portion ofthe STV signal between two consecutive drops of the plurality of drops.The computing system is also configured to generate a pulse-averagedsignal based at least partially upon the plurality of pulse periods. Thecomputing system is also configured to generate an amplitude envelopebased at least partially upon the pulse-averaged signal. The computingsystem is also configured to generate a meniscus carrier signal based atleast partially upon the pulse-averaged signal, the amplitude envelope,or both. The computing system is also configured to determine a meniscusoscillation frequency of the drops in the nozzle based at leastpartially upon the meniscus carrier signal.

A 3D printer configured to print a 3D object by jetting a plurality ofdrops of liquid metal of onto a substrate is also disclosed. The 3Dprinter includes an ejector having a nozzle. The 3D printer alsoincludes a heating element configured to heat a solid metal within theejector, thereby converting the solid metal to the liquid metal. The 3Dprinter also includes a coil wrapped at least partially around theejector. The 3D printer also includes a power source configured totransmit voltage pulses to the coil. The coil causes the plurality ofdrops of the liquid metal to be jetted through the nozzle in response tothe voltage pulses. The 3D printer also includes a camera configured tocapture a video of the drops being jetted through the nozzle. The 3Dprinter also includes a light source configured to illuminate the nozzleand the drops as the video is captured. The 3D printer also includes acomputing system configured to determine a location of the nozzle in thevideo. The computing system is also configured to determine aspatiotemporal variance (STV) signal at the location of the nozzle inthe video. The computing system is also configured to determine when thedrops are jetted through the nozzle by identifying a neighboringlocation proximate to the location of the nozzle, determining a secondSTV signal at the neighboring location, and determining that the dropsare jetted through the nozzle in response to increases in the second STVsignal, which indicates that the drops have been jetted through thenozzle and are passing through the neighboring location. The computingsystem is also configured to determine a plurality of pulse periodsbased at least partially upon the determination of when the drops arejetted through the nozzle. Each pulse period includes a portion of theSTV signal between two consecutive drops of the plurality of drops. Thecomputing system is also configured to generate a pulse-averaged signalby aligning and averaging the plurality of pulse periods. The computingsystem is also configured to generate an amplitude envelope by moving asliding temporal window over the pulse-averaged signal. The amplitudeenvelope includes a difference between local maxima and minima over thesliding temporal window. The computing system is also configured togenerate a meniscus carrier signal by normalizing the pulse-averagedsignal to zero mean to produce a normalized pulse-averaged signal, anddividing the normalized pulse-averaged signal by the envelope amplitudeto generate the meniscus carrier signal. The meniscus carrier signal isin a time domain. The computing system is also configured to determine ameniscus oscillation frequency of menisci on lower surfaces of the dropspositioned at least partially within in the nozzle by converting themeniscus carrier signal from the time domain to a frequency domain, andlocating a peak of the meniscus carrier signal in the frequency domain.The computing system is also configured to adjust a parameter of the 3Dprinter based at least partially upon the meniscus oscillationfrequency.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate embodiments of the presentteachings and together with the description, serve to explain theprinciples of the disclosure. In the figures:

FIG. 1 depicts a schematic cross-sectional view of a 3D printer,according to an embodiment.

FIG. 2 depicts a side view of a portion of FIG. 1 , according to anembodiment.

FIG. 3 depicts a flowchart of a method for printing a 3D object,according to an embodiment.

FIG. 4 depicts a frame (e.g., an image) from a video that identifies alocation of a nozzle of the 3D printer, according to an embodiment.

FIG. 5 depicts a schematic view of an example of a 3D view of the videoof the nozzle, according to an embodiment.

FIG. 6 depicts a graph of an example STV signal (i.e., waveform) overtime at the location of the nozzle, according to an embodiment.

FIG. 7 depicts a frame (e.g., an image) from the video, according to anembodiment.

FIG. 8 illustrates a STV waveform, a pulse-averaged signal, an envelopeamplitude signal, a meniscus carrier signal in the time domain, and ameniscus oscillation frequency in the frequency domain, according to anembodiment.

DETAILED DESCRIPTION

Reference will now be made in detail to exemplary embodiments of thepresent teachings, examples of which are illustrated in the accompanyingdrawings. Wherever possible, the same reference numbers will be usedthroughout the drawings to refer to the same, similar, or like parts.

During 3D printing with a liquid metal, the liquid metal is separatedinto individual drops within a nozzle of the 3D printer, and the dropsare jetted (also referred to as ejected) one by one in a discretemanner. The lower surface of a drop may have a meniscus while the dropis positioned at least partially in the nozzle (e.g., just prior tobeing jetted). The behavior of the meniscus may provide informationabout the internal characteristics of the inside the 3D printer (e.g.,the nozzle) that affect the properties of the jetting and/or propertiesof the printed 3D metallic object formed by the drops. Thus, the presentdisclosure is directed to characterizing the meniscus of the (e.g.,lower surface) drops while the drops are positioned at least partiallywithin the nozzle.

In order for the drops to be consistent in form and motion, the meniscusoscillation after one drop is jetted through the nozzle may decayrapidly and acquiesce before the next drop is jetted. In one embodiment,the meniscus behavior may be characterized by capturing high-speedvideos of the nozzle during printing, and performing visual assessmentof the surface behavior of the meniscus between jettings. In anotherembodiment, the characterization of the meniscus behavior may beautomated. For example, the meniscus behavior may be quantified usingvideo analysis and/or by machine learning (ML) to the high-speed videos.

FIG. 1 depicts a schematic cross-sectional view of a 3D printer 100,according to an embodiment. The 3D printer 100 may include an ejector(also referred to as a pump chamber) 110. The ejector 110 may define aninner volume that is configured to receive a printing material 120. Theprinting material 120 may be or include a metal, a polymer, or the like.For example, the printing material 120 may be or include aluminum (e.g.,a spool of aluminum wire).

The 3D printer 100 may also include one or more heating elements 130.The heating elements 130 are configured to melt the printing material120 within the inner volume of the ejector 110, thereby converting theprinting material 120 from a solid material to a liquid material (e.g.,liquid metal) 122 within the inner volume of the ejector 110.

The 3D printer 100 may also include a power source 132 and one or moremetallic coils 134. The metallic coils 134 are wrapped at leastpartially around the ejector 110 and/or the heating elements 130. Thepower source 132 may be coupled to the coils 134 and configured toprovide power thereto. In one embodiment, the power source 132 may beconfigured to provide a step function direct current (DC) voltageprofile (e.g., voltage pulses) to the coils 134, which may create anincreasing magnetic field. The increasing magnetic field may cause anelectromotive force within the ejector 110, that in turn causes aninduced electrical current in the liquid metal 122. The magnetic fieldand the induced electrical current in the liquid metal 122 may create aradially inward force on the liquid metal 122, known as a Lorenz force.The Lorenz force creates a pressure at an inlet of a nozzle 114 of theejector 110. The pressure causes the liquid metal 122 to be jettedthrough the nozzle 114 in the form of one or more drops 124.

The 3D printer 100 may also include one or more cameras (one is shown:140) that is/are configured to capture video and/or images of the nozzle114, the drops 124, or both. In one embodiment, the video may includesignals derived from a sequence of images. In another embodiment, theimages may be or include frames of the video. In one particular example,a strobe construction of the jetting may be achieved by capturing aplurality of individual images/frames with different time delays frommultiple drop ejections. The camera 140 may be or include a high-speedcamera that is configured to capture the images and/or video at a rateof from about 2,000 frames per second to about 50,000 frames per secondor about 10,000 frames per second to about 30,000 frames per second(e.g., 19,000 frames per second). In one example, the jetting may occurat a frequency from about 100 Hz to about 1000 Hz, and the camera 140may operate at a frequency from about 10,000 frames per second to about50,000 frames per second. In one embodiment, front face monitoringduring the jetting of the drops may be triggered by the 3D printer 100as a normal checkup, operator intervention, detection of irregularjetting, and/or by detection of greater than usual deviations of the 3Dprinter 100.

The 3D printer 100 may also include one or more light sources (one isshown: 150) that is/are configured to shine light on the nozzle 114, thedrops 124, or both. The light source 150 may be or include a fiber opticlight source. The light source 150 may be or include a collimated lightsource. The light source 150 may be or include a white light source.

The 3D printer 100 may also include a substrate (also referred to as abuild plate) 160 that is positioned below the nozzle 114. The drops 124that are jetted through the nozzle 114 may land on the substrate 160 andcool and solidify to produce a 3D object 126. The substrate 160 mayinclude a heater 162 therein that is configured to increase thetemperate of the substrate 160. The 3D printer 100 may also include asubstrate control motor 164 that is configured to move the substrate 160as the drops 124 are being jetted (i.e., during the printing process) tocause the 3D object 126 to have the desired shape and size. Thesubstrate control motor 164 may be configured to move the substrate 160in one dimension (e.g., along an X axis), in two dimensions (e.g., alongthe X axis and a Y axis), or in three dimensions (e.g., along the Xaxis, the Y axis, and a Z axis). In another embodiment, the ejector 110and/or the nozzle 114 may be also or instead be configured to move inone, two, or three dimensions.

In one embodiment, the 3D printer 100 may also include an enclosure 170.The enclosure 170 may be positioned at least partially around theejector 110, the nozzle 114, the drops 124, the 3D object 126, theheating elements 130, the coils 134, the camera 140, the light source150, the substrate 160, or a combination thereof. In one embodiment, theenclosure 170 may be hermetically sealed. In another embodiment, theenclosure 170 may not be hermetically sealed. In other words, theenclosure 170 may have one or more openings that may allow gas to flowtherethrough. For example, the gas may flow out of the enclosure 170through the openings.

In one embodiment, the 3D printer 100 may also include one or more gassources (one is shown: 180). The gas source 180 may be positionedoutside of the enclosure 170 and configured to introduce gas into theenclosure 170. The gas source 180 may be configured to introduce a gasthat flows (e.g., downward) around the ejector 110, the nozzle 114, theheating elements 130, or a combination thereof. The gas may flow aroundand/or within the coils 134. The gas may flow into the enclosure 170and/or proximate to (e.g., around) the drops 124, the 3D object 126,and/or the substrate 160.

The 3D printer 100 may also include a gas sensor 182. The gas sensor 182may be positioned within the enclosure 170. The gas sensor 182 may alsoor instead be positioned proximate to the drops 124, the 3D object 126,and/or the substrate 160 (e.g., in an embodiment where the enclosure 170is omitted). The gas sensor 182 may be configured to measure aconcentration of the gas, oxygen, or a combination thereof.

The 3D printer 100 may also include a computing system 190. Thecomputing system 190 may be configured to control the introduction ofthe printing material 120 into the ejector 110, the heating elements130, the power source 132, the camera 140, the light source 150, thesubstrate control motor 164, the gas source 180, the gas sensor 182, ora combination thereof. For example, the computing system 190 may beconfigured to receive the images and/or video from the camera 140 and tocharacterize the behavior of the meniscus on the lower surface of thedrops 124 while the drops are positioned at least partially within thenozzle 114. The computing system 190 may also be configured to adjustone or more parameters of the 3D printer based at least partially uponthe behavior of the meniscus. The behavior of the meniscus and/or theadjustment of the parameters may be part of a real-time closed loopcontrol system provided by the computing system 190.

FIG. 2 depicts a side view of a portion of FIG. 1 , according to anembodiment. More particularly, FIG. 2 depicts a side view of the nozzle114, the camera 140, and the light source 150. In FIG. 2 , five drops124A-124E of the liquid printing material 120 are shown. The drop 124Ais positioned at least partially within the nozzle 114, and the drops124B-124E have already been jetted from the nozzle 114 and aredescending toward the substrate 160 (not shown in FIG. 2 ).

The camera 140 and/or the light source 150 may be directed at/toward atleast a portion of the liquid printing material 120 that is positionedat least partially within the nozzle 114. Said another way, the camera140 and/or the light source 150 may be directed at/toward at one of thedrops 124A that is positioned at least partially within the nozzle 114(e.g., before the drop 124A has been jetted from the nozzle 114). Moreparticularly, the camera 140 and/or the light source 150 may be directedat/toward a meniscus 125 of the liquid printing material 120 (e.g., drop124A) that is positioned at least partially within the nozzle 114. Themeniscus 125 refers to the convex and/or crescent shape of the lowersurface of the liquid printing material 120 (e.g., drop 124A). In theembodiment shown, the meniscus 125 is at least partially outside (e.g.,below) the lower end of the nozzle 114. In another embodiment (notshown), the meniscus 125 may be at least partially or fullyinside/within the nozzle 114, such that the lowermost part of the liquidprinting material 120 (or drop 124A) would not be seen in the side viewof FIG. 2 .

FIG. 3 depicts a flowchart of a method 300 for printing the 3D object126, according to an embodiment. More particularly, the method 300 maycharacterize a behavior of the meniscus 125 of the liquid printingmaterial 120 (e.g., the drop 124A) when the drop 124A is positioned atleast partially within the nozzle 114. The method 300 is particularlyapplicable to liquid metal drops 124A-124E in 3D printing applications(as opposed to non-metal drops and/or non 3D printing applications)because metallic drops produce specular highlights, and analysis ofthese specular highlights provided by the method 300 may provideinformation and insight into the oscillation of the liquid metal surface(e.g., the meniscus 125) of the drop 124A. The behavior (e.g.,oscillation) of the meniscus 125 may be directly related to thestability of the drop 124A, which in turn affects the quality of the 3Dobject 126.

An illustrative order of the method 300 is provided below; however, oneor more steps of the method 300 may be performed in a different order,performed simultaneously, repeated, or omitted. One or more steps of themethod 300 may be performed (e.g., automatically) by the computingsystem 190.

The method 300 may include illuminating the nozzle 114 with the lightsource 150, as at 302. This may include illuminating the liquid printingmaterial 120 (e.g., drop 124A) positioned at least partially within thenozzle 114. For example, this may include illuminating the meniscus 125of the liquid printing material 120 (e.g., drop 124A) positioned atleast partially within the nozzle 114.

The method 300 may also include capturing a video of the nozzle 114, asat 304. This may include capturing a video of the liquid printingmaterial 120 (e.g., drop 124A) positioned at least partially within thenozzle 114. For example, this may include capturing a video of themeniscus 125 of the liquid printing material 120 (e.g., drop 124A)positioned at least partially within the nozzle 114. This step may alsoinclude capturing a video of the drop 124A as the drop 124A descendsfrom the nozzle 114 (e.g., to the substrate 160). As mentioned above, asused herein, a video may include a plurality of images. Thus, this stepmay also or instead include capturing a plurality of images of thenozzle 114.

The method 300 may also include determining a location 115 of the nozzle114 in the video, as at 306. The location 115 of the nozzle 114 may bedetermined by the computing system 190 based at least partially upon thevideo. FIG. 4 depicts a frame (e.g., an image) 400 from the video thatidentifies the location 115 of the nozzle 114, according to anembodiment. For each x-y location in the video, a 3D window may bepositioned (e.g., centered) around that location, and the 3D window maybe run over a temporal dimension in a sliding fashion. A spatiotemporalvariance (STV) may be determined over time proximate to the nozzle 114.The minimum STV value may be identified and may as an indicator ofdrop-induced motion at that spatial location, denoted V_(xy). Thisprocess may be repeated over the two spatial dimensions x-y, and the x-ylocation that maximizes V_(xy) may be determined to be the location 115of the nozzle 114. The location 115 may be a 2D or 3D box. One or moreof the following steps of the method 300 may be performed within thelocation 115 (e.g., the box representing the nozzle region). Thelocation 115 may also be referred to as the region of interest (ROI).

The geometry of the 3D surface of the meniscus 125 may be difficult tomeasure precisely; however, the behavior (e.g., movement, motion, etc.)of the meniscus 125 may manifest itself in the video in terms of aspatiotemporal pattern of specular highlights. As used herein, “specularhighlights” refer to the phenomenon of light reflecting off of the frontsurface of the liquid (e.g., the drop 124A). More particularly, thespecular highlight(s) may move as the meniscus 125 moves, and themovement of the specular highlight(s) may be used to determine or inferthe characteristics of the (movement of the) meniscus 125. Thus, oncethe location 115 of the nozzle 114 has been determined, the method 300may also include determining (e.g., measuring and/or plotting) a STVsignal over time at the location 115 of the nozzle 114, as at 308. TheSTV signal may be determined by the computing system 190 based at leastpartially upon the video (e.g., using a spatiotemporal variance-guidedfilter (SVGF)). As described below, the STV signal may be used todetermine the behavior of the meniscus 125 over time.

FIG. 5 depicts a schematic view of an example of a 3D view of the video500 of the nozzle 114, according to an embodiment. The video 500 isplotted as a temporal waveform. The STV signal may be measured within awindow within the video 500. For example, the STV signal may be measuredwithin an M×M×K spatiotemporal window within the video 500, where M isthe spatial extent in pixels, and K is the number of frames. In oneembodiment, the STV signal may be determined as the variance of pixelscomputed over time at one or more M×M fixed spatial locations (e.g.,locations 510, 520), and then the average of these variances at the M×Mfixed spatial locations 510, 520 may be determined to obtain an averageSTV signal for the 3D block. Below the 3D view of the video 500 is theSTV signal 530, which includes the average 512 of the M×M fixed spatiallocation 510 and the average 522 of the M×M fixed spatial location 520.

FIG. 6 depicts a graph 600 of an example STV signal (i.e., waveform) 610over time at the location 115 of the nozzle 114, according to anembodiment. In the graph 600, the X axis represents the temporal frameindex, and the Y axis represents the computed STV.

The method 300 may also include determining when the drops 124A-124E arejetted through the nozzle 114, as at 310. To accomplish this, one ormore neighboring locations, proximate to the location 115 of the nozzle114, may be searched, and the neighboring location with the largest STVmay be determined to be the one through which the drops 124A-124EW passafter being jetted through the nozzle 114. Increases in the STV signalin this neighboring location 116 may identify the jetting of the drops124A-124E (i.e., the drop ejections).

FIG. 7 depicts a frame (e.g., an image) 700 from the video 500,according to an embodiment. In the image 700, the location 115 and theneighboring location 116 are shown. For example, in FIG. 7 , the rightside of the image 700 represents up, the left side of the image 700represents down, and the drop 124A is moving from right/up to left/down.Referring to FIGS. 6 and 7 , it may be seen that the meniscus 125 of thedrop 124A reaches a predetermined (e.g., low) steady state level (i.e.,a desirable level) before the next drop is jetted. As used herein, thepredetermined steady state level is less than about 30%, less than about20%, or less than about 10% of the maximum STV within the pulse period.

The method 300 may also include determining one or more metrics based atleast partially upon the STV signal, as at 312. The metrics may be usedto characterize the (e.g., aggregate) behavior of the meniscus 125 overthe temporal duration of the video 500. Thus, determining the metricsmay include determining pulse periods (four pulse periods are shown:811-814) between times at which the drops 124A-124E are jetted (threetimes are shown 821-823), as at 314. This determination may be madebased at least partially upon the STV signal. This is shown in the graph810 in FIG. 8 .

Determining the one or more metrics may also include generating apulse-averaged signal (i.e., waveform) based at least partially upon thepulse periods, as at 316. The pulse-averaged signal is shown in thegraph 830 in FIG. 8 . Generating the pulse-averaged signal may includealigning the STV signal in the pulse periods 811-814 and/or averagingthe STV signal in the pulse periods 811-814.

Determining the one or more metrics may also include generating anamplitude envelope A(t) based at least partially upon the pulse-averagedsignal, as at 318. As used herein, the “amplitude envelope” refers tothe difference between local maxima and local minima over a slidingtemporal window. This envelope may yield information about the decayrate and time taken for the meniscus 125 to reach a “quiet” steadystate. The amplitude envelope is shown in the graph 840 in FIG. 8 .Generating the amplitude envelope may include moving a sliding temporalwindow over the pulse-averaged signal. The amplitude envelope may be adifference between local maxima and minima over the sliding temporalwindow. The amplitude envelope may yield information about an amount oftime and/or a decay rate for the meniscus 125 to reach a quiet steadystate.

Determining the one or more metrics may also include generating ameniscus carrier signal based at least partially upon the pulse-averagedsignal (in graph 830) and the amplitude envelope (in graph 840), as at320. The meniscus carrier signal is shown in the graph 850 in FIG. 8 .Generating the meniscus carrier signal may include normalizing thepulse-averaged signal to zero mean to produce a normalized pulse-averagesignal, and then dividing the normalized pulse-average signal by theenvelope amplitude. The meniscus carrier signal may be in the timedomain.

Determining the one or more metrics may also include determining ameniscus oscillation frequency based at least partially upon themeniscus carrier signal, as at 322. The meniscus oscillation frequencyis shown in the graph 860 in FIG. 8 . The meniscus oscillation frequencymay be determined by locating a peak in a Fourier Transform of themeniscus carrier signal. As shown in the graph 860, the peak amplitudeoccurs at about 600 Hz oscillation.

The method 300 may also include determining (e.g., characterizing) abehavior of the meniscus 125, as at 324. The behavior may bedetermined/characterized based at least partially upon the metrics(e.g., the characteristics of the pulse STV waveform). Moreparticularly, the behavior may be determined/characterized based atleast partially upon the pulse periods (in graph 810), thepulse-averaged signal (in graph 830), the envelope amplitude (in graph840), the meniscus carrier signal (in graph 850), the meniscusoscillation frequency (in graph 860), or a combination thereof. In oneembodiment, determining/characterizing may include quantifying thebehavior of the meniscus 125. This may yield a more objective assessmentof the meniscus 125 in comparison to conventional visual inspection ofthe video.

For example, the behavior of the meniscus may be determined to beoptimal when the meniscus oscillation frequency (in graph 860) is in afirst range, and the behavior of the meniscus may be determined to besub-optimal when the meniscus oscillation frequency (in graph 860) is ina second range. The first range may be from about 500 Hz to about 2 kHzor from about 1 kHz to about 1.5 kHz. The second range may be aboveand/or below the first range. The oscillation may be a combination oftransverse and surface waves.

In at least one embodiment, the method 300 may also include predictingjetting quality of the 3D printer 100, as at 326. The jetting qualitymay be predicted using a machine learning (ML) algorithm. The MLalgorithm may be trained using features extracted from the video 500.The features may be or include the pulse periods (in graph 810), thepulse-averaged signal (in graph 830), the envelope amplitude (in graph840), the meniscus carrier signal (in graph 850), the meniscusoscillation frequency (in graph 860), or a combination thereof. Thefeatures may also or instead include a principal component analysis(PCA) on the pulse-averaged signal (in graph 830), a carrier frequencyof the STV signal, a pulse-to-pulse covariance of the STV signal, apulse-to-pulse correlation of the STV signal, a mean and/or max of thepulse-averaged signal (in graph 830), a decay of the STV signal withineach pulse period 811-814, or a combination thereof. The ML algorithmmay also be trained by labeling ground truths that indicate the qualityof jetting. The ground truths may be labelled by a user. In one example,three quality labels may be used: good, overactive, and hyperactive. Asused in this context, “good” refers to the meniscus amplitude decayingmonotonically between pulses, and the behavior is repeatable from pulseto pulse, “overactive” refers to the meniscus oscillation having a highamplitude in between pulses, and the behavior is generally repeatablefrom pulse to pulse, and “hyperactive” refers to the meniscusoscillation having a high amplitude in between pulses, and the behavioris not repeatable from pulse to pulse.

In one embodiment, the features and quality labels from the training setare provided to a Random Forest (RF) classifier to predict a qualityclass. In one example, a “quality class” refers to one of good,overactive, and hyperactive. The RF classifier may include a pluralityof trees (e.g., 300 trees). A test configuration that provided goodresults used the following features: the carrier frequency of the STVsignal, the pulse-to-pulse covariance of the STV signal, the mean and/ormax of the pulse-averaged signal (in graph 830), and the decay of theSTV signal within each pulse period 811-814. This resulted in aprediction accuracy of 86% on one test. It was observed that confusionwas greatest between “good” and “overactive” classes.

In one embodiment, the method 300 may also include predicting astability of the drops 124A-124E, as at 328. Drop stability refers tothe consistency between drops 124A-124E in a continuous jetting mode.Drop stability may be quantified on a scale from 1 (poor) to 4(excellent) (e.g., based on visual assessment of strobe videos of thedrops 124A-124E). The RF classifier may be trained using one or more offeatures mentioned above that are used to train the ML algorithm. In onetest, using a random forest classifier with 100 trees, a predictionaccuracy of 60% was achieved.

The steps 326, 328 may also or instead use other variants of MLalgorithms (e.g., support vector machine, artificial neural network,etc.) and/or additional features (e.g., autocorrelation of waveform, andfeatures automatically learned from the data using a deep neuralnetwork).

The method 300 may also include adjusting one or more parameters of the3D printer 100, as at 330. The parameters may be adjusted based at leastpartially upon the one or more metrics (e.g., the pulse periods (ingraph 810), the pulse-averaged signal (in graph 830), the envelopeamplitude (in graph 840), the meniscus carrier signal (in graph 850),the meniscus oscillation frequency (in graph 860), or a combinationthereof). For example, the one or more parameters may be adjusted inresponse to the meniscus oscillation frequency being greater than apredetermined threshold (e.g., 1.5 kHz). In another example, the one ormore parameters may be adjusted in response to a decay rate of themeniscus oscillation frequency being greater than or less than apredetermined rate.

The parameters to be adjusted may be or include power (e.g., voltage,current, frequency, pulse length, voltage vs time waveform, etc.)provided to the coils 134 by the power source 132. For example, if thebehavior of the meniscus 125 is determined (at 324) to be overdriven,the power delivered to the coils 134 may be decreased. As used herein,“overdriven” means excess energy is being added to the liquid metal 122in the ejector 110, which results in excess residual energy in themeniscus 125. The parameters may also or instead include the amount ofheat generated by the heating elements 130, the temperature of theliquid printing material 120 (e.g., the drops 124A-124E), the size ofthe drops 124A-124E, the frequency at which the drops 124A-124E areejected, or a combination thereof.

Notwithstanding that the numerical ranges and parameters setting forththe broad scope of the present teachings are approximations, thenumerical values set forth in the specific examples are reported asprecisely as possible. Any numerical value, however, inherently containscertain errors necessarily resulting from the standard deviation foundin their respective testing measurements. Moreover, all ranges disclosedherein are to be understood to encompass any and all sub-ranges subsumedtherein. For example, a range of “less than 10” may include any and allsub-ranges between (and including) the minimum value of zero and themaximum value of 10, that is, any and all sub-ranges having a minimumvalue of equal to or greater than zero and a maximum value of equal toor less than 10, e.g., 1 to 5.

While the present teachings have been illustrated with respect to one ormore implementations, alterations and/or modifications may be made tothe illustrated examples without departing from the spirit and scope ofthe appended claims. For example, it may be appreciated that while theprocess is described as a series of acts or events, the presentteachings are not limited by the ordering of such acts or events. Someacts may occur in different orders and/or concurrently with other actsor events apart from those described herein. Also, not all processstages may be required to implement a methodology in accordance with oneor more aspects or embodiments of the present teachings. It may beappreciated that structural objects and/or processing stages may beadded, or existing structural objects and/or processing stages may beremoved or modified. Further, one or more of the acts depicted hereinmay be carried out in one or more separate acts and/or phases.Furthermore, to the extent that the terms “including,” “includes,”“having,” “has,” “with,” or variants thereof are used in either thedetailed description and the claims, such terms are intended to beinclusive in a manner similar to the term “comprising.” The term “atleast one of” is used to mean one or more of the listed items may beselected. Further, in the discussion and claims herein, the term “on”used with respect to two materials, one “on” the other, means at leastsome contact between the materials, while “over” means the materials arein proximity, but possibly with one or more additional interveningmaterials such that contact is possible but not required. Neither “on”nor “over” implies any directionality as used herein. The term“conformal” describes a coating material in which angles of theunderlying material are preserved by the conformal material. The term“about” indicates that the value listed may be somewhat altered, as longas the alteration does not result in nonconformance of the process orstructure to the illustrated embodiment. The terms “couple,” “coupled,”“connect,” “connection,” “connected,” “in connection with,” and“connecting” refer to “in direct connection with” or “in connection withvia one or more intermediate elements or members.” Finally, the terms“exemplary” or “illustrative” indicate the description is used as anexample, rather than implying that it is an ideal. Other embodiments ofthe present teachings may be apparent to those skilled in the art fromconsideration of the specification and practice of the disclosureherein. It is intended that the specification and examples be consideredas exemplary only, with a true scope and spirit of the present teachingsbeing indicated by the following claims.

What is claimed is:
 1. A 3D printer, comprising: a nozzle; a cameraconfigured to capture an image, a video, or both of a liquid metal beingjetted through the nozzle; and a computing system configured to: measurea signal proximate to the nozzle based at least partially upon theimage, the video, or both; and determine a meniscus oscillationfrequency of the liquid metal in the nozzle based at least partiallyupon the signal.
 2. The 3D printer of claim 1, wherein the computingsystem is further configured to predict a jetting quality of the 3Dprinter based at least partially upon the meniscus oscillationfrequency.
 3. The 3D printer of claim 1, wherein the computing system isfurther configured to predict a stability of drops of the liquid metalbased at least partially upon the meniscus oscillation frequency.
 4. The3D printer of claim 1, wherein the computing system is furtherconfigured to adjust a parameter of the 3D printer based at leastpartially upon the meniscus oscillation frequency.
 5. The 3D printer ofclaim 4, wherein the parameter comprises a current, a voltage, a pulselength, a voltage versus time waveform, or a combination thereofprovided to a coil of the 3D printer that causes the liquid metal to bejetted through the nozzle.
 6. The 3D printer of claim 4, wherein theparameter comprises a frequency at which the liquid metal is jettedthrough the nozzle.
 7. A 3D printer configured to print a 3D object, the3D printer comprising: a nozzle; a camera configured to capture a videoof a liquid metal being jetted through the nozzle; and a computingsystem configured to: determine a signal at a location of the nozzle inthe video; determine a plurality of pulse periods based at leastpartially upon the signal, wherein each pulse period comprises a portionof the signal between two consecutive drops of the liquid metal afterbeing ejected from the nozzle; generate a pulse-averaged signal based atleast partially upon the plurality of pulse periods; generate anamplitude envelope based at least partially upon the pulse-averagedsignal; generate a meniscus carrier signal based at least partially uponthe pulse-averaged signal, the amplitude envelope, or both; anddetermine a meniscus oscillation frequency of the liquid metal in thenozzle based at least partially upon the meniscus carrier signal.
 8. The3D printer of claim 7, wherein generating the amplitude envelopecomprises moving a sliding temporal window over the pulse-averagedsignal, and wherein the amplitude envelope comprises a differencebetween local maxima and minima over the sliding temporal window.
 9. The3D printer of claim 7, wherein generating the meniscus carrier signalcomprises: normalizing the pulse-averaged signal to zero mean to producea normalized pulse-averaged signal; and dividing the normalizedpulse-averaged signal by the envelope amplitude to generate the meniscuscarrier signal, wherein the meniscus carrier signal is in a time domain.10. The 3D printer of claim 7, wherein determining the meniscusoscillation frequency comprises: converting the meniscus carrier signalfrom a time domain to a frequency domain; and locating a peak of themeniscus carrier signal in the frequency domain.
 11. The 3D printer ofclaim 7, wherein the computing system is further configured to adjust aparameter of the 3D printer based at least partially upon the meniscusoscillation frequency.
 12. A 3D printer configured to print a 3D objectby jetting a plurality of drops of a liquid metal of onto a substrate,the 3D printer comprising: an ejector comprising a nozzle; a heatingelement configured to heat a solid metal within the ejector, therebyconverting the solid metal to the liquid metal; a coil wrapped at leastpartially around the ejector; a power source configured to transmitvoltage pulses to the coil, wherein the coil causes the plurality ofdrops of the liquid metal to be jetted through the nozzle in response tothe voltage pulses; a camera configured to capture a video of the liquidmetal being jetted through the nozzle; a light source configured toilluminate the nozzle and the liquid metal as the video is captured; anda computing system configured to: determine a location of the nozzle inthe video; determine a signal at the location of the nozzle in thevideo; determine when the drops are ejected from the nozzle by:identifying a neighboring location proximate to the location of thenozzle; determining a second signal at the neighboring location; anddetermining that the drops are ejected from the nozzle in response toincreases in the second signal, which indicates that the drops have beenejected from the nozzle and are passing through the neighboringlocation; determine a plurality of pulse periods based at leastpartially upon the determination of when the drops are ejected from thenozzle, wherein each pulse period comprises a portion of the signalbetween two consecutive drops of the plurality of drops; generate apulse-averaged signal by aligning and averaging the plurality of pulseperiods; generate an amplitude envelope by moving a sliding temporalwindow over the pulse-averaged signal, wherein the amplitude envelopecomprises a difference between local maxima and minima over the slidingtemporal window; generate a meniscus carrier signal by: normalizing thepulse-averaged signal to zero mean to produce a normalizedpulse-averaged signal; and dividing the normalized pulse-averaged signalby the envelope amplitude to generate the meniscus carrier signal,wherein the meniscus carrier signal is in a time domain; determine ameniscus oscillation frequency of the liquid metal positioned at leastpartially within in the nozzle by: converting the meniscus carriersignal from the time domain to a frequency domain; and locating a peakof the meniscus carrier signal in the frequency domain; and adjust aparameter of the 3D printer based at least partially upon the meniscusoscillation frequency.
 13. The 3D printer of claim 12, wherein adjustingthe parameter comprises adjusting an amplitude of the voltage pulsestransmitted to the coil.
 14. The 3D printer of claim 12, whereinadjusting the parameter comprises adjusting a frequency of the voltagepulses transmitted to the coil.
 15. The 3D printer of claim 12, whereinadjusting the parameter comprises adjusting a size of the drops.
 16. The3D printer of claim 12, wherein adjusting the parameter comprisesadjusting a temperature of the liquid metal in the ejector.
 17. The 3Dprinter of claim 1, wherein determining the meniscus oscillationfrequency of the liquid metal in the nozzle comprises determining themeniscus oscillation frequency of the liquid metal in the nozzle beforethe liquid metal is ejected from the nozzle as a plurality of drops.